Comparison of single and ensemble classifiers in terms of accuracy and execution time

Classification accuracy and execution time are two important parameters in the selection of classification algorithms. In our experiments, 12 different ensemble algorithms, and 11 single classifiers are compared according to their accuracies and train/test time over 36 datasets. The results show that Rotation Forest has the highest accuracy. However, when accuracy and execution time are considered together, Random Forest and Random Committees can be the best choices.